Foundational Frameworks for Strategic Growth
Foundational Frameworks for Strategic Growth
Corporate structural design helps us better understand where strategic advantages arise.
Introduction
In the last piece, I observed that high skilled workers within the Innovation Economy are increasingly unfulfilled and proposed that this malaise results directly from the relatively restrictive structures or paradigms we currently offer workers today. It’s a very rough framework and one that I will most certainly need to expand and refine over time, but it at least lays the groundwork for further exploration into what such paradigmatic structural innovations may look like.
Fulfillment and productivity are not orthogonal, nor are they perfectly aligned. But they are, I believe, highly positively correlated, and thus if we can design systems that enable greater levels of fulfillment in our most innovative people, we should also yield greater productivity as well.
If the first piece was worker focused, this piece will shift our focus to the structural foundations of the corporation itself. There is substantial intellectual and theoretical ground to cover here, which I hope will provide sufficient context for a more direct exploration of how we might evolve our corporate structures to both improve total productivity and enhance fulfillment across a broader swath of our best knowledge workers.
Each component I discuss below has emerged from more than a decade of scholarship from my intellectual spirit animal,
. His pieces are quite dense, so forgive me as I annotate a greater number of quotes than I typically would in this newsletter.Economies of Scale, Scope, and Variety
We start with an exploration of the different “economies” that are potentially available to corporations today. The most well-known are the economies of scale, which are:
the advantages that can result when repeatable processes are used to deliver large volumes of identical products or service instances. Scaling relies on interchangeable parts either in the product itself, or in the delivery mechanisms, in the case of intangible services.
Economies of scale emerge as production efficiencies effectively amortize costs across a larger and larger batch of identical products, driving down the cost to the end consumer.
“When you step back and think about all this, you realize that scaling is basically the equivalent of deliberate practice (the 10,000 hours idea) for companies. The COO is typically the unsung hero leading this scaling process (and often is promoted to CEO during the transition to a scaling phase).
Less known (I certainly hadn’t heard of it before) are the economies of scope. Unlike scale, scope advantages emerge not from repeatable production of the same product but:
are the advantages that can result when similar processes are used to deliver a set of distinct products or services.
“Economies of scope is an economic theory stating that average total cost of production decreases as a result of increasing the number of different goods produced. The essential reason for economies of scope is some substantial joint cost across the production of multiple products.”
The quintessential manifestation of this concept is a CPG conglomerate like Procter & Gamble, who have, over time, built up the capacity for leveraging their centralized production to spin out numerous different products.
The advantages of scope economies are themselves voluminous:
“Extreme flexibility in product design and product mix
Rapid responses to changes in market demand, product design and mix, output rates, and equipment scheduling
Greater control, accuracy, and repeatability of processes
Reduced costs from less waste and lower training and changeover costs
More predictability (e.g., maintenance costs)
Less risk: A company that sells many product lines, sells in many countries, or both will benefit from reduced risk (e.g., if a product line falls out of fashion or if one country has an economic slowdown, the company will likely be able to continue trading)”
On this last point regarding risk mitigation, diversified portfolio principles are apt because scope companies have assembled what amounts to a diversified portfolio of products.
Now that we’ve established the bases for Scale vs. Scope, a more simplistic way to think about each of these is to contrast the core function of each:
“scaling is engineering, scoping is marketing, both are types of learning, you have to do both to survive in competitive markets, which are the only kind around today.”
These core functions also manifest in the hypothetical “hierarchies of power” within a company. Generally I would argue that because engineering is the core in scaling economy companies, engineering as a discipline has the loudest voice in the room. Conversely, scope economy companies like P&G are led primarily by marketers, who must do whatever possible to better position their (often commodity) products against substantial competition.
“Each of these questions is driven by a market structure concern that can be phrased as “should this be traded via the marketplace or managed through internal cost accounting?” The answers have some engineering implications, but they are marketing questions first. They help determine the operational shape, size and boundaries of the firm relative to its financial size. Scoping decisions lead to scaling commitments.”
A final point of comparison is the natural equilibrium into which each economy settles:
“Unlike scale learning, which can reasonably be expected to plateau into an efficient state that will then deliver high-margin revenues for a period, scope learning may never plateau at all.”
A given economy of scale is likely composed of a single S-curve, but the n products in a “scope portfolio”, each of which may have its own individual S-curve, combined have no single point of equilibrium. There is no obvious optimal state here.
The topic of this section includes three concepts, yet I’ve spent the entirety focusing solely on the first two. The third type of economy represents a different flavor entirely:
“If economies of scale and scope are about doing the thing right, economies of VARIETY are about doing the right thing.”
Translating this into more standard Silicon Valley-speak:
Scope & Scale assume product-market fit has already been achieved.
Variety helps to discover product-market fit.
“Economies of variety are the result of learning through variation (in the sense of trying a variety of things looking for product-market fit for example), with benefits realized as creative innovation capacity distributed throughout the system.”
Though I don’t think Venkatesh conceptualized it this way, it’s easy to see how these three economies may be viewed as (potentially) sequential:
Variety comes first as it is all about mass experimentation and learning. It says nothing about “operational efficiency” like the other two.
This variety CAN manifest in scope, but the two are independent. Variety becomes scope if the inputs for each variety can be leveraged over and over for greater efficiencies of launching and running many different businesses.
Scope does not mean a lack of scale - individual pieces of scope may themselves drive economies of scale, especially in the case of mass manufacturing like in various CPG conglomerates. But not all businesses will have economies of scale, and economies of scale do not require scope or variety (especially when the given business makes one product, for example.
Even if these three CAN be sequential, they are in no way path dependent or deterministic - a company may exhibit any of the three and never gain access to either of the other two.
“Just as the prize for winning economies of scale is a highly favorable amortization-of-fixed-costs equation, and the prize for winning economies of scope is a highly favorable pattern of transaction costs, the prize for winning economies of variety will be a highly efficient learning capability that will result in a sort of “dynamic Coasean” firm that will have the equivalent of a primate brain.”
Or more succinctly:
“How you learn determines how you scale.”
Fat and Lean
Ok, that was a lot to digest, but let’s expand the concepts even further into what Venkatesh calls “Fat vs. Lean Thinking”. Let’s immediately anchor this to our three economy types:
“To get to economies of scale and scope, you have to think lean. To get to economies of variety on the other hand, you have to think fat.”
As we already established, both Scale and Scope ultimately represent a company state after already having established product-market fit, and the emphasis is on extracting as much value from this product (portfolio) as possible. The goal here is, ultimately, efficiency, and thus these activities are lean.
In the pre-product-market fit stage, efficiency is simply not possible. Economies of variety are specifically designed to experiment heavily, which in absolute terms is very expensive and/or wasteful. Learning is quite expensive because so many of the experiments simply can’t work. Learning is, generally, a “fat” activity.
On its face I think it makes total sense, but within a broader context it’s potentially counterintuitive. The Lean Startup movement has taken off the past decade or so, but lean here refers mostly to low costs and simplistic products. Such “lean products” could be part of a “fat company” if they are part of a larger experimentation engine, but most companies are not structured this way1.
Remember that the Economies of Scope, Scale, and Variety are ultimately modes of learning, not specifically about smallness or largeness of companies (or budgets). That said, lean activities (Scope & Scale) will typically be taken on by larger companies specifically because they are post product-market fit, and thus their approach to capital allocation looks quite different:
“Lean is really defined by two imperatives, both of which fat thinking violates:
Minimizing the amount of invested capital required to do something (so you need less money locked up in capital assets and inventory)
Maximizing the rate of return on that invested capital (through, broadly, minimizing downtime, or equivalently, time to recover from failures).”
And since a running theme of this newsletter is exploring how to increase our total innovative capacity, I’d be remiss if I didn’t include these (not short) observations about the broader macro-economic impact of Fat and Lean learning:
“[In] an entire economy full of lean-thinking, six-sigma-ing, customer-listening corporations, … companies live long and prosper, as captives of a rentier, cronyist class. But they produce very little by way of truly novel products and services. So the stability comes at the cost of slow eroding economic dynamism and increasing fragility of the national and global economies they are part of.”
Whew, ok. What about the opposite?
“[In] an entire economy full of fat-thinking, unique-snowflaking, product-driven corporations, … companies live free or die hard, at very high rates, churning rapidly, and produce a great many new products and services, most of which fail. But in so failing, they add economic dynamism and antifragility to the national and global economies they are part of.”
Now, obviously neither of these poles is feasible in practice, but my bias for a much heavier weighting of fat economies should be obvious.
GUTS Framework
We’ve now established the first two components of this broader framework of the corporation:
Scale, Scope, and Variety define the types of learning environments tied ultimately to the given company’s maturity and product strategy.
Fat and Lean define the relative costs and efficiencies of these learning environments.
The final component we’re missing is tying these more concretely to the inputs and outputs of our work systems. Venkatesh calls his version of this framework GUTS:
The Grand Unified Theory of Striving (or Slacking)
It looks like this:
The first way to look at this is the Left-Right hemispheres, corresponding to the Fat and Lean principles we just discussed:
Activity on the left is Lean because there are clear paths forward. This work can be efficient (typically measured in, say, time to complete) precisely because the work ahead consists primarily of known knowns.
Activity on the right is Fat because there is no obvious path forward - there are a host of unknowns that must be dealt with, and thus there is considerable expensive, inefficient work to be done to even figure out how to proceed.
Rao calls these clear paths forward the “Critical Paths” and colors them red.
These represent the pieces of our work pathways that are, yes, critical to the completion of the work.
Our Lean work, with its higher concentration of known knowns, enables at least one end-to-end critical path, with the primary difference separating Projects from Exploration being that in the former, there is a single critical path precisely because there is a singular outcome that must be achieved.
The green lines correspond to additional work paths that are not critical, and may or may not enable us to achieve our goals. In a Lean mindset we want to limit green because it represents waste - it is activity that, whether we can a priori see it as such is not critical to our goal achievement.
And that brings us to another way to view our work, corresponding to the fuzzy brownish shading in each quadrant.
Let’s call these goals, and thus we again have a new way to think about our work, this time comparing the top and bottom hemispheres.
Rao calls this “why axis” 🙄 Convergent vs. Divergent work. I find this classification more challenging to keep clear in my head and instead think of this as output versus input-oriented work:
Convergence (the bottom two) starts from a known output and then plots the course to that output. What matters is delivering the output.
Divergence (the top two) starts without a known output and instead focuses on the inputs, with the expectation that such input-focused work will, over time, yield interesting outputs.
This all comes back to the certainty of goals:
The bottom two quadrants have a single desirable outcome.
The top two quadrants instead have many potential outcomes of valuable.
“Knowing why gives you one half of the information you need to optimize things: the objective function, such as time to market or delivery.”
This is, in other words, knowing what outcome is preferred.
A Project is one where there is a clear, known path from the current state to the outcome.
Muddling Through is when it’s not at all clear where to even start to achieve the given outcome, so VARIETY and Darwinian learning are required such that the end state may emerge from these disparate activities.
Another way of thinking about the why/how distinction is ambiguity vs. uncertainty, where ambiguity means the outcomes are unknown and uncertainty means the pathways are unknown.
“Low ambiguity tolerance will drive you towards focus (divergence to convergence) via why-context restriction. You don't mind improvising, but you want to be sure you're getting somewhere meaningful, like Mars.”
Those uncomfortable with ambiguity require clear outcomes, and thus they tend to stick to work in the bottom two quadrants.
“Low uncertainty tolerance will drive you towards global criticality (fat to lean) via how-context restriction. You don't care where you go, but you want to get there efficiently, and professionally, and possibly win "best stonecutter" along the way.”
In contrast, those uncomfortable with uncertainty will stick to the left two quadrants - Lean work - because they can focus on what they’re great at, their trade, inputs.
So those are the core components of the GUTS framework:
The x-axis represents Lean vs Fat work.
The y/why-axis represents Goal Certainty
Red lines represent critical pieces of work
Green lines represent non-critical pieces of work.
Brown fuzzies represent the goal(s)
Your head may still be spinning so let’s actually put these components into action.
Project
“You're in a convergent and lean operating context. The dreaded bottom-left doghouse in the 2x2. You know why you're acting, and you know how to act. This is a project.”
This is the quadrant where much consulting work is handled. It’s (almost) entirely about execution, which is appealing for some types of people, but it leaves very little room for creativity, which is NOT appealing for a large swath of people.
Because this is on the LEAN side, it is hyper-focused on efficiency, such as delivery times and cutting wastes, precisely because these are largely known knowns, and thus “slack in the system” (the green lines) is both unnecessary and unwanted. The goal here is delivery, not discovery.
Note that there still exist a preponderance of green lines, of slack in the system - this indicates that even if the critical path is clear, there can still be considerable complexity in the problem set - supply chains, for example. But the end state is singular and clear, and thus a lean, convergent PROJECT.
Exploration
“But the smart response to not knowing the answer to why is to simply pursue many, divergent goals that test different motivations. And this pursuit can be efficient even if its ends are unclear. Through exploration, you expose your thinking to novelty, so you might discover entirely unexpected answers to "why" in your peripheral vision.”
An obvious example here is trying out multiple musical instruments. The HOW is clear, and thus this is a LEAN activity - you simply need to try out different instruments. But the WHY is more of an emergent property - you’re not setting out to discover one specific instrument, but think directionally that playing an instrument might be interesting.
Or if we want to focus on a specific profession, think here about artists and artisans. Each has a specific set of skills (the HOW) and very much enjoys the work itself, the inputs. There is rarely one localized goal; the “ultimate creation”. Instead, there is more of a globalized goal of creating - and the only path forward is to keep focusing on craft, and great art will eventually emerge from the many, many iterations.
Most work today, and specifically most corporate work, does not operate within this quadrant. The clearest manifestation of this type of approach in corporate land is Startup Studios, a structure I’ll be focusing on heavily in the next piece and thus will keep things brief here. The basic premise is leveraging known tools, systems, and structures for creating novel products/companies, and repeating this process over and over - input optimization without clear outputs.
Muddling Through
“When goals are big and complex, but still defined enough that they can serve as a singular focus, you muddle through, iteratively refining means and ends as you figure it out.”
The quintessential muddling through is the Apollo mission. The why was super clear - go to the moon - but getting there was so complex, with so many disparate uncertainties, that there literally could not be a critical path. There was instead a set of fat tasks, a whole lot of multi-dimensional exploration, even though the “north star” remained clear.
Because we’re on the Fat side of the spectrum, this work can be quite expensive, and thus we’ll typically not see startups compete here. Instead this is the quadrant primarily for governments and mega-corporations.
Play
“Finally, we have the top right quadrant, which is always the best quadrant in quadrantology. Here you have no good answers to either why or how.”
This is not typically the realm of capitalistic endeavors. No, this work is typically (far) upstream in the realm of academia and pure science. The first question this work tries to answer is “is this even possible?” If we stopped there it might shift into Exploration work. But there’s the second challenge of, if it’s possible, how do we even demonstrate that it is?
This the ultimate sandbox of uncertainty and ambiguity; you could fish in these waters for an entire career and not actually reach any satisfying answers. We absolutely need this work, and more of it! Ben Reinhardt & Speculative Technologies are doing some really interesting structural innovation in the space, but with programs of research like “Nanomodular Electronics”, commercialization is years away (at least).
Economies + GUTS
We should also tie GUTS back to our initial discussion of the three economy variants that companies can deploy:
A quick rationale for these:
Economies of scale are all about know-how and repetitive structures that lead to massive cost efficiencies. This looks mostly like Project work, with a singular critical path, though there may also be a bit of muddling through that is enabled by the large-scale banking of free cash flow from the project work.
Economies of Scope are similarly Lean endeavors, but rather than starting from a singular outcome, this work is more focused on how to extract multiple different products from the same type of work. This looks mostly like Exploration work, and if successful, this may enable a bit of Play, typically in the form of an internal R&D group.
Economies of Variety function quite differently than the other two. We initially discussed this activity as being on the Fat side, and thus within the Play realm - there is a whole lot of experimentation here that typically results in no solid results. But I also think this extends roughly equally into the Exploration quadrant, when Variety activities largely flow out of a known set of primitives that can be used over and over again.
Putting it all together
This piece is challenging precisely because it almost entirely consists of frameworks and hypotheticals. I’ll admit that it took me multiple readings of Rao’s Economies, Fat/Lean, and GUTS frameworks to establish even a baseline understanding of how they all work together. And I don’t think I’m fully there yet!
But this type of systemic structural work is necessary to more assertively and confidently solve for the challenge I posed in the last piece - how can we innovate on the structures of work itself to improve both productivity (Innovative Capacity) and worker fulfillment?!
I’ll attempt a first pass on such an applied endeavor next week.
This is specifically the focus of the next piece.